本文選題：遙感 + 作物�。� 參考：《農(nóng)業(yè)工程學(xué)報(bào)》2017年15期

【摘要】：為客觀獲取寧夏水稻面積空間分布信息,也為區(qū)域農(nóng)作物遙感監(jiān)測奠定技術(shù)基礎(chǔ),該文以寧夏回族自治區(qū)為研究區(qū)域,選擇美國LandSat-8攜帶的陸地成像儀(operational land imager,OLI)數(shù)據(jù),采用2016年3月11日-7月01日間的15景影像,基于水稻田耕地與水體特征反射率隨著季節(jié)變化規(guī)律的分析,采用歸一化植被指數(shù)(normalized difference vegetation index,NDVI)、近紅外波段反射率(infrared reflectance,IR)、短波指數(shù)(short waved index,SWI)3個(gè)指數(shù),以及多時(shí)相NDVI最大值、IR最小值、SWI最小值3個(gè)衍生指數(shù),共6個(gè)指數(shù)為基礎(chǔ)進(jìn)行決策分類樹構(gòu)建,對全區(qū)水稻進(jìn)行識別與提取,采用該區(qū)水稻面積本底遙感調(diào)查結(jié)果進(jìn)行精度驗(yàn)證,水稻種植面積提取誤差僅.4.22%,Kappa系數(shù)為0.83,水稻空間分布的用戶分類精度分別為85.11%,制圖精度為81.67%;同時(shí)與監(jiān)督分類方法提取的水稻面積進(jìn)行對比,該文方法提取水稻的用戶精度提高了8.13個(gè)百分點(diǎn),制圖精度更是提高了20.01個(gè)百分點(diǎn)。研究結(jié)果表明,利用中高分辨率的OLI遙感時(shí)間序列衛(wèi)星影像,在大宗農(nóng)作物時(shí)間序列的變化規(guī)律分析基礎(chǔ)上,構(gòu)建分類決策樹,可以準(zhǔn)確地提取大宗農(nóng)作物種植面積,是區(qū)域農(nóng)作物面積遙感監(jiān)測業(yè)務(wù)運(yùn)行中具有潛力的方法。
[Abstract]:Objective to obtain rice in Ningxia area of spatial information, but also lays the foundation for regional crop remote sensing monitoring, this paper takes the Ningxia Hui Autonomous Region as the study area, land imager choose America carrying LandSat-8 (operational land imager, OLI) data, the March 11, 2016 -7 month 01 day 15 image, paddy cultivated land and water features with reflectance analysis of seasonal variation based on the normalized difference vegetation index (normalized difference vegetation index, NDVI), near infrared reflectance (infrared reflectance, IR (short waved), short wave index index, SWI) 3 index, and multitemporal NDVI maximum IR minimum, the minimum value of SWI 3 derivative index, total the 6 index based decision classification tree, recognition and extraction of the rice, the remote sensing survey results are accuracy test using rice area of the district C, rice planting area extraction error is only.4.22%, the Kappa coefficient is 0.83, the user classification accuracy of spatial distribution of rice were 85.11%, mapping accuracy is 81.67%; at the same time, rice area extraction and supervised classification methods were compared, the extraction method of rice user accuracy increased by 8.13 percentage points, more is to improve the mapping accuracy 20.01 percentage points. The results show that the use of OLI time series remote sensing satellite images with high resolution, the variation in staple crops in time series based on the analysis of the construction of decision tree classification, can accurately extract the planting area of main crops, is a potential method of regional crop area remote sensing monitoring business operation.

【作者單位】： 中國農(nóng)業(yè)科學(xué)院農(nóng)業(yè)資源與農(nóng)業(yè)區(qū)劃研究所;
【基金】：國家重點(diǎn)研發(fā)計(jì)劃“糧食作物生長監(jiān)測診斷與精確栽培技術(shù)”課題“作物生長與生產(chǎn)力衛(wèi)星遙感監(jiān)測預(yù)測”(2016YFD0300603)
【分類號】：S127;S511

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